Detecting and monitoring traffic within a transport network can require CCTV networks, ticketing information, inductive loops to detect vehicles or survey data. However, these techniques are expensive to set up, lack accuracy and cannot effectively determine when an individual has changed from one mode of transport to another during a particular route or journey. Other techniques for monitoring travel patterns use GPS receivers within mobile devices (“Real-Time Urban Monitoring Using Cell Phones: a Case Study in Rome”—Calabrese, F., Ratti, C., Colonna, M., Lovisolo, P., and Parata, D. IEEE Transactions on Intelligent Transportation Systems, 12(1), 141-151, 2011). Whilst this can provide very accurate results, many travellers do not habitually carry a GPS receiver and even when they do it may be deactivated most of the time due to limited battery life. Therefore, the number of journeys monitored may be low and reduce accuracy or wide scale monitoring.
Mobile operators may collect network usage records (NUR) that contains information about call events including telephone calls, SMS and mobile data retrieval. The NUR data include information about where the call was initiated and terminated, its duration and the parties (or telephone numbers) involved in the particular event. These data may be used by a billing system to account for events initiated by a subscriber using a handset.
“Transportation Mode Inference from Anonymized and Aggregated Mobile Phone Call Detail Records”—Huayong Wang, Francesco Calabrese, Giusy Di Lorenzo, Carlo Ratti: 13th IEEE International Conference on Intelligent Transportation Systems, September 2010, describes using mobile phone call detail records (a particular type of NUR) to estimate a particular transportation mode share for a particular origin and destination and how this changes over time. The described system may be used to investigate the particular modes of transport that a group of travellers (with an active mobile phone) use between two particular points. This is achieved by determining how fast a traveller was travelling between these two set points and from this calculated speed, determine the likely mode of transport that was used. However, this method cannot determine between modes of transport that operate with similar speeds. Furthermore, particular start and end points must be defined and only modes of transport between these exact points are considered. Therefore, this system is limited to journeys with well defined start and end points. The system also required manual filtering and analysis of particular data records in order to achieve sensible results.
Therefore, there is required a method that overcomes these problems.